b4c72f0d5e
Imported from GitHub PR #32792 - Add command line argument to choose momentum optimizer option to speech_commands. - Default optimizer for speech commands is unchanged to still use GradientDescentOptimizer. - Default values for momentum optimizer are to use a momentum of .9 and use_nesterov=True. Copybara import of the project: - 6a2c6007fff318ba9f6f8e4ff335c3089335c774 Add momentum optimizer option to speech_commands by Michael Wang <michael@companionlabs.ai> - ffdebcfa55fb9b82fba3add6884b6da0bf9cbde0 Use compat.v1.MomentumOptimizer by Mark Daoust <markdaoust@google.com> - 0168e4a6ab93596d2010caa458eb715d16a2a24a Fix formatting by Michael Wang <michael@companionlabs.ai> - 19b930f7d42a327bed9cb560b5577f50532a76d7 Merge 0168e4a6ab93596d2010caa458eb715d16a2a24a into b5c48... by mswang12 <40969844+mswang12@users.noreply.github.com> COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/32792 from Companion-Labs:momentum_speech_commands 0168e4a6ab93596d2010caa458eb715d16a2a24a PiperOrigin-RevId: 274079593
139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for data input for speech commands."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import unittest
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import tensorflow as tf
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from tensorflow.examples.speech_commands import train
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from tensorflow.python.framework import test_util
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from tensorflow.python.platform import gfile
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from tensorflow.python.platform import test
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def requires_contrib(test_method):
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try:
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_ = tf.contrib
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except AttributeError:
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test_method = unittest.skip(
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'This test requires tf.contrib:\n `pip install tensorflow<=1.15`')(
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test_method)
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return test_method
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# Used to convert a dictionary into an object, for mocking parsed flags.
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class DictStruct(object):
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def __init__(self, **entries):
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self.__dict__.update(entries)
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class TrainTest(test.TestCase):
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def _getWavData(self):
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with self.cached_session():
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sample_data = tf.zeros([32000, 2])
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wav_encoder = tf.audio.encode_wav(sample_data, 16000)
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wav_data = self.evaluate(wav_encoder)
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return wav_data
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def _saveTestWavFile(self, filename, wav_data):
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with open(filename, 'wb') as f:
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f.write(wav_data)
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def _saveWavFolders(self, root_dir, labels, how_many):
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wav_data = self._getWavData()
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for label in labels:
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dir_name = os.path.join(root_dir, label)
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os.mkdir(dir_name)
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for i in range(how_many):
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file_path = os.path.join(dir_name, 'some_audio_%d.wav' % i)
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self._saveTestWavFile(file_path, wav_data)
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def _prepareDummyTrainingData(self):
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tmp_dir = self.get_temp_dir()
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wav_dir = os.path.join(tmp_dir, 'wavs')
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os.mkdir(wav_dir)
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self._saveWavFolders(wav_dir, ['a', 'b', 'c'], 100)
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background_dir = os.path.join(wav_dir, '_background_noise_')
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os.mkdir(background_dir)
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wav_data = self._getWavData()
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for i in range(10):
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file_path = os.path.join(background_dir, 'background_audio_%d.wav' % i)
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self._saveTestWavFile(file_path, wav_data)
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return wav_dir
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def _getDefaultFlags(self):
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flags = {
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'data_url': '',
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'data_dir': self._prepareDummyTrainingData(),
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'wanted_words': 'a,b,c',
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'sample_rate': 16000,
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'clip_duration_ms': 1000,
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'window_size_ms': 30,
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'window_stride_ms': 20,
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'feature_bin_count': 40,
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'preprocess': 'mfcc',
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'silence_percentage': 25,
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'unknown_percentage': 25,
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'validation_percentage': 10,
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'testing_percentage': 10,
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'summaries_dir': os.path.join(self.get_temp_dir(), 'summaries'),
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'train_dir': os.path.join(self.get_temp_dir(), 'train'),
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'time_shift_ms': 100,
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'how_many_training_steps': '2',
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'learning_rate': '0.01',
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'quantize': False,
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'model_architecture': 'conv',
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'check_nans': False,
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'start_checkpoint': '',
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'batch_size': 1,
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'background_volume': 0.25,
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'background_frequency': 0.8,
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'eval_step_interval': 1,
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'save_step_interval': 1,
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'verbosity': tf.compat.v1.logging.INFO,
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'optimizer': 'gradient_descent'
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}
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return DictStruct(**flags)
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@test_util.run_deprecated_v1
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def testTrain(self):
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train.FLAGS = self._getDefaultFlags()
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train.main('')
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self.assertTrue(
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gfile.Exists(
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os.path.join(train.FLAGS.train_dir,
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train.FLAGS.model_architecture + '.pbtxt')))
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self.assertTrue(
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gfile.Exists(
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os.path.join(train.FLAGS.train_dir,
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train.FLAGS.model_architecture + '_labels.txt')))
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self.assertTrue(
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gfile.Exists(
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os.path.join(train.FLAGS.train_dir,
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train.FLAGS.model_architecture + '.ckpt-1.meta')))
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if __name__ == '__main__':
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test.main()
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